Search Results for: doug kanter

Visualizing Blood Glucose

 

For people who take insulin, self-measurement is a matter of life and death. No wonder, then, that people with diabetes who track their blood glucose have been so important in advancing techniques of visualization,and understanding data. At the Quantified Self Europe conference in Amsterdam this year, we were honored to host a panel discussion on Data Visualization and Meaning with Joel Goldsmith (Abbott Diabetes Care), Jana Beck (Tidepool), Doug Kanter (Databetes), and Stefanie Rondags (diabetes coach and blogger).

This discussion strikes me as widely important for self-trackers whether or not we have diabetes. Many  of us will be tracking blood glucose in the near future. And the issues of data access, understanding, and clinical relevance that people with diabetes are working on resemble challenges commonly faced by anybody who is tracking for health.

For instance, Jana Beck was asked during the Q&A about her health care providers. How receptive are they to the important experiments she’s done to improve her health based on the data she’s collected? ”None of my endocrinologists have been very receptive to this approach,” she answered. “My A1C tends to fall within the range of what’s considered the gold range for people with Type 1. But I’m interested in optimizing that further. Often, I don’t even see them more than twice a year.”

Jana, Stefanie, and Doug all showed their own data in the context of discussing experiments and decisions that have had a major impact on their wellbeing. All were clear that the domain of these experiments and decisions is not healthcare as traditionally understood; but nor is it a matter of general fitness or lifestyle. The domain of these experiments is different and perhaps still unnamed. Self-collected data can and should essential health decisions, but the most advanced techniques of understanding this data are still being developed in an ad-hoc, grassroots way, by knowledgeable and open minded individuals who have a strong interest in learning for themselves.

At the end of the session I asked Joel Goldsmith, of Abbott Diabetes care, about the future prospects of the Freestyle Libre, a minimally invasive wearable blood glucose monitor that is not yet available in the US. (Disclosure: Abbott Diabetes Care was one of the sponsors of the QS Europe Conference.) The Freestyle Libre has a sensor in the form of a patch worn on the arm, and a touchscreen reader device that you lift close to the sensor to get a reading. There is no finger prick involved. While this and competing minimally invasive or non-invasive glucose monitors will almost certainly continue to be regulated as medical devices and understood as part of the health care system, many other people will also use them, and the flood of data and the questions that go with it will challenge our understanding of where this type of information should live.

The video above contains the full session, including the Q&A.

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Quantified Self and Apple’s ResearchKit

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Apple’s announcement of ResearchKit is strong evidence that Quantified Self practices are emerging as a major influence on medical research and other forms of knowledge making.

Apple talked about how their new effort focused on opening up health research is designed to combat five main current issues:

  • Limited Participation
  • Small sample sizes limit our understanding of diseases
  • Reliance on subjective data
  • Infrequent data provide only snapshots through time
  • One-way communication from researcher to participant (and only at the end of the study, if at all)

Furthermore, the design of ResearchKit allows the  participant to decide how data is shared. Apple will not see the data. Participants are allowed to be involved in the data collection in real-time, using the data they’re collecting to understand and inform their own health improvement plans.

In light of today’s announcement we wanted to highlight some of our favorite and most powerful examples of taking the research process into one’s own hands, making their own knowledge through thoughtful data collection and reflection. We invite you watch what’s possible now, and imagine with us what could be accomplished tomorrow.

Mark Drangsholt: Deciphering My Brain Fog

Lindsay Meyer on Tracking Hearing Loss

Thomas Christiansen on Learning from 60,000 Observations

Nan Shellabarger: 26 Years of Weight Tracking

Rob Rothfarb on Tracking My Blood

Last year we gather a fantastic group of researchers, toolmakers, and science leadership at the 2014 Quantified Self Public Health Symposium to discuss how personal data can impact personal and public health. That meeting culminated in a great report that touches on many of the aspects discussed today regarding ResearchKit. We invite you to download, read, and share that report. For a more nuanced look into how ResearchKit may impact the research community, we’re highlighting four great talks from the the meeting.

Susannah Fox shares research from the Pew Internet and Life Project and describes the challenges ahead for promoting self-tracking.

Margaret McKenna explores the issues, challenges, and ideas large scale self-tracking applications have in mind when they consider working with the research community.

Jason Bobe talks about the lessons learned from involving research participants in the data ownership and discovery process.

Doug Kanter describes what he’s learned from tracking and visualizing his diabetes data.

If you’re interested in how ResearchKit will be affecting self-tracking, personal data, and access to information, research and knowledge making, then stay tuned to our Access Channel here on QuantifiedSelf.com and on Medium.

We are sure to have many great talks and sessions that focus on ResearchKit at our QS15 Conference and Actrivate Exposition. We invite you to join us.


We invite you to share your data access stories, and this article with the #qsaccess hashtag and follow along on quantifiedself.com and @quantifiedself.

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Visualizing Our Quantified Self

At our 2013 Quantified Self Global Conference we were excited to share a variety of beautiful and insightful data visualizations from our community. In the months leading up to the conference we asked attendees to send in their own personal data visualizations along with a short description. In our 6 years of hosting Quantified Self meetups and events, as well as running this website, our forum, and social channels, we’ve seen the power of data visualization as a story telling medium. We exist in part to help people tell their stories – about the data they collect, the changes they create, and the insights and new knowledge they’re excited to share.

Today we’re sharing a few of our favorite visualizations from past conferences. The images and descriptions below represent a wide a variety of tracking experiences and techniques, and we hope to showcase eve more unique personal data projects at our upcoming QS15 Conference & Exposition.

Tracking Sleep by Anita Lillie

This is concatenation of screenshots from my sleep app. Most sleep apps don’t let you zoom out like this and still see daily/nightly detail, so I just made it myself. I like that it shows how almost-consistent I am with my sleep, and made me ask new questions about the “shape” of a night of sleep for me.



2.5 Years of My Weight by Mette Dyhrberg

I gained a lot of insights from this heat map. The most obvious weight gain was no surprise — that’s when I periodically don’t track. In any case, the big picture patterns are easily identified with a heat map. Realized looking at this heat map that the point of no return was mid-April 2012 — my data shows that was when I switched protein shakes with an egg based breakfast. I have since experimented and seen that protein shake in the morning seems to keep my blood sugar more stable and as a result my weight under control!



One Month of Blood Sugar by Doug Kanter

This is a visualization of one month of my blood sugar readings from October 2012. I see that my control was generally good, with high blood sugars happening most often around midnight (at the top of the circle).



Tracking Productivity by Nick Winter

My percentile feedback graph of my development productivity helps my motivation.



Six Months of My Life by David El Achkar

This is my life during the past six months. Each square = 15 minutes. Each column = 1 day. This picture represents 138 days or 3,000+ activities.



My Thesis Self Portrait by Sara M. Watson

Here’s a period of a few days of webcam images taken using Stan James’ LifeSlice during the final days of editing my thesis on Quantified Self uses of personal data. Serious business!



Sleep and Meaningful Work by Robby Macdonell

In an average work day, I don’t consider communication (email, instant message, etc) to be terribly meaningful work. I’d much rather be working on building software. Getting more sleep the night before increases the amount of meaningful work I’m likely to do in a day.



70 Days of Pulse by Laurie Frick

Pulse rate over 24 hours for 70 days from my Basis watch. Grey=null, blues=85

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QS | Public Health Symposium: Doug Kanter’s Healthiest Year

We’ve featured the work of our friend and QS community member, Doug Kanter, many times here on the Quantified Self website and we were excited to have him participate in our Quantified Self Public Health Symposium. Doug is both a toolmaker and self-tracker, focusing primarily on using his experience with tracking his diabetes-related data to inform new tools and methods. In this talk, Doug explains what he learned from diving headfirst into a year-long project of tracking and visualizing all of the data he could gather about his diabetes self-management, his diet and activity, and other important factors. Beyond the wonderful visualizations he shared, Doug helped highlight something many patients and self-trackers are struggling with, the inability to access data easily and the lack of interoperability among data services and devices. We invite you to watch Doug’s wonderful talk below.

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Diabetes, Metabolism, and the Quantified Self

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This is a visualization of one month of my blood sugar readings from October 2012. I see that my control was generally good, with high blood sugars happening most often around midnight (at the top of the circle). -Doug Kanter

Richard Bernstein, an engineer with diabetes, pioneered home blood glucose monitoring. What he learned about himself contradicted the medical doctrine of his day, but Bernstein went on to become an MD himself, and established a thriving practice completely devoted to helping others with diabetes. We think of Dr. Bernstein as a hero because he used self-measurement to support his own learning, and shared what he learned for general benefit.

Tracking personal metabolism is a necessity for diabetics, and it is also something that will become increasingly common for many people who want to understand and improve their metabolism. Diabetics are also leading the fight for personal access to personal data, and we’re looking forward to meeting inspiring activists and toolmakers today at the DiabetesMine D-Data Exchange meeting in San Francisco. In honor of this meeting, we’ve put together an anthology of sort of QS Show&Tell talks about diabetes and metabolism data.

Jana Beck
Jana is a Type 1 diabetic and data visualization practitioner who has been working on creating new techniques for understanding that data from her Dexcom continuous blood glucose monitor. In this talk, she described some of her newest techniques and her ongoing work with Tidepool.org. You can also view her original QS show&tell talk here.

Doug Kanter
Doug has been featured here on the QS website many times. We first learned about Doug through his amazing visualizations of his own data (like the image above). At the 2013 QS Global Conference, Doug shared what he learned from tracking his diabetes, diet, activity, and other personal data and his ongoing work with the Databetes project.

We spoke with Doug about his experience with tracking, visualizing and understanding his diabetes data. You can listen to that below.

James Stout
James is a graduate student, professional cyclist, and a Type 1 diabetic. In this talk at the QS San Diego meetup group he talked a bit about how he manages his diabetes along with his near super human exercise schedule and how he uses his experience to inspire others. (Check out this great article he wrote for Ride Magazine.)

Brooks Kincaid
Brooks, a Type 1 diabetic, was tracking his blood glucose manually for years before switching to a continuous blood glucose meter. In this talk he describes what he’s learned from his data and why he prefers a modal day view.

Bob Troia
Bob tracked his fasting blood glucose, diet, and activity to find out what could help him lower his risk of developing type 2 diabetes.

Vivienne Ming
Vivienne’s son was diagnosed with Type 1 Diabetes two years ago and she’s applied her scientific and data analysis background to understand her son’s life.

Seth Roberts
Seth has a long history of tracking and experimenting with his metabolic data. In one of his last QS talks, he spoke about how alternate day fasting was impacting his blood sugar.

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Conference Preview: Bob Troia on Tracking Blood Glucose

We’ve learned a lot from the diabetics in our community, such as Jana Beck’s lessons from 100,000+ blood glucose readings, and Doug Kanter’s narrative visualizations of a year of his diabetes data. At the upcoming QS Europe Conference on May 10th and 11th in Amsterdam, we’re going to hear the interesting story of a non-diabetic who began tracking his fasting glucose to improve his health.

With the US Centers for Disease Control estimating that over a third of the US population shows signs of diabetes or pre-diabetes, it’s not surprising that the techniques of learning from blood glucose measurements are spreading more widely. After learning from his 23andMe profile that he had an elevated risk for developing Type 2 Diabetes, Bob Troia began tracking his fasting glucose daily while also tracking exercise, diet, and  experimenting with supplements. He’s been reporting the results on his blog, Quantified Bob. If you’re curious about how to apply these techniques in our own life, join as at the upcoming meeting, or keep an eye out for the video of Bob’s talk at the New York Quantified Self show&tell.

The 2014 Quantified Self Europe Conference is just a few weeks away. Please join us!

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What We Are Reading

Another collection of thought-provoking items from around the web.

Articles & Posts

Plan to move from #quantified self to Qualified Self by Inga de Waard. Every now and then someone writes something that causes me to pump the brakes and really reflect on self-tracking and personal data collection. This is one of those time. Inga does a nice job here setting up her experience with self-tracking to understand her type 1 diabetes. She moves on to explore how “qualified data” might be a better source of information for personal growth, “I am more than my body, I am mind. So I want to understand more.”

The Bracelet of Neelie Kroes (in German) by Frank Schirrmacher. Can machines be trusted? Are we building and willingly wearing the handcuffs of the future by strapping tracking devices to our wrists? These questions are explored in this article. (If you’re like me you are probably wondering who Neelie Kroes is. Here’s some background info.)

Biggest Gene Sequence project to launch by Bradley J. Fikes and Gary Robbins. J. Craig Venter is at it again. Now that genome sequencing has passed the $1000 barrier he has set up a new company in order to recruit and sequence 40,000 people per year.

This Mediated Life by Christopher Butler. Another amazing piece of self-reflection spawned by the recently released Reporter App. Rather than reviewing the application, the author addresses what it means to self-track when we know we are our own observer. Do we bias our reflection and data submission when we know that each answer, each data point is being collected into a larger set? (This post reminded me of one of my favorite movie lines, “How am I not myself.” from I Heart Huckabees

The Open Collar Project. At a recent meeting I learned of this project to create an open-source dog tracking collar. Pet trackers are becoming more prevalent in the market, but the purpose of this project goes far beyond just understanding pet activity. I learned from the lead researcher, Kevin Lhoste, that they’re using this as a method to encourage and engage children in science and mathematics. Very neat stuff.

Twitch Crowdsourcing: Crowd Contributions in Short Bursts of Time [PDF] by Rajan Vaish, Keith Wyngarden, Jingshu Chen, Brandon Cheung, and Michael S. Bernstein. This research paper describes the results of a really interesting project to gather information from people using micro-transactions during the phone unlocking process. It appears that we can learn a lot from people in under 2 seconds.

The Open FDA. Not an article here, but I wanted to call attention to the new open initiative by the FDA. This new effort was spearheaded by Presidential Innovation Fellow, Sean Herron. If you’re interested in doing this type of work you can apply to be a fellow here.

Show&Tells (a selection of first person stories on self-tracking and personal data)

200 days of stats: My QS experience by Octavian Logigan. Octavian recounts the various data he’s collected including activity, sleep, email behavior, and work productivity. I really like how he clearly explains what tools he’s using.

A Year in Diabetes Data by Doug Kanter. We’ve featured Doug here on the blog before. From his amazing visualizations to his talks about his process, we’ve been consitently impressed and inspired by this work. In this post Doug recounts 2012 – “[...] the healthiest year of my life.” (Full disclosure: Doug sent me the poster version of his data and it is beautiful.)

Visualization

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This visualization comes to us from Tim Kim, a design student based in Los Angeles.

The map shows different collections and documentations made during my cross country trip. Posts made during the trip on various social media sites are orientated and placed by the geological locations. The states are elongated by purely how I felt about the duration of going across the specific state. For example, driving through texas sucked (no offense). Different facts are layered and collaged across the map to create and express a collective, over-all image of the trip. Some quantifiable information, some quantitative information to create a psych-geolocal map.

Thumbs Up Viz A really nice website that highlights and explains the good pieces of data visualization popping up all over the web these days.

From the Forum

Tracking emotional experience
Test our a new app for sleep improvement
Measuring emotions through vital signs

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QS Gallery: Doug Kanter

Doug Kanter shared this beautiful and unique visualization of his blood glucose with us. Be sure to take a peak at his other great visualizations and his wonderful talk at the 2013 Quantified Self Global Conference.

This is a visualization of one month of my blood sugar readings from October 2012. I see that my control was generally good, with high blood sugars happening most often around midnight (at the top of the circle).
-Doug Kanter

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Doug Kanter: A Year Of Diabetes Data

The complex relationship between behavior and diabetes control has long been a testing ground for gathering and making sense of personal data. Doug Kanter is a Type-1 diabetic who’s been thinking about how self-tracking influences his diabetes control for a few years. While in graduate school at the Interactive Telecommunications Program (ITP) at NYU he started experimenting with visualizations that helped him understand his blood sugar and insulin dosing. In 2012 he began adding more data to his exploration in order to better understand how diet played a role in his diabetes self-management. Watch this great talk to learn more about Doug’s journey and his ongoing Databetes project.

We’ll be posting videos from our 2013 Global Conference during the next few months. If you’d like see talks like this in person we invite you to join us in Amsterdam for our 2014 Quantified Self Europe Conference on May 10 and 11th.

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Doug Kanter on Data, Diabetes, and Marathon Training

Doug Kanter has been a Type 1 diabetic for 26 years. Through this time he’s come to learn more about his disease by using many data-gathering tools and his own work in visual analysis at the NYU ITP program. We’ve featured Doug’s compelling work here on the blog before and we were excited to hear him talk at the NY QS Meetup about his new project to understand how marathon training and running effect his blood sugar and insulin treatment.

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